Chapter 1, Part III: Proofs Summary  Valid Arguments and Rules of Inference  Proof Methods  Proof Strategies Section 1.6 Section Summary  Valid Arguments  Inference Rules for Propositional Logic  Using Rules of Inference to Build Arguments  Rules of Inference for Quantified Statements  Building Arguments for Quantified Statements

Revisiting the Socrates Example  We have the two premises:  “All men are mortal.”  “Socrates is a man.”  And the conclusion:  “Socrates is mortal.”  How do we get the conclusion from the premises? The Argument  We can express the premises (above the line) and the conclusion (below the line) in predicate logic as an argument:

 We will see shortly that this is a valid argument. Valid Arguments  We will show how to construct valid arguments in two stages; first for propositional logic and then for predicate logic. The rules of inference are the essential building block in the construction of valid arguments. 1. Propositional Logic Inference Rules 2. Predicate Logic Inference rules for propositional logic plus additional inference rules to handle variables and quantifiers. Arguments in Propositional Logic  A argument in propositional logic is a sequence of propositions. All but the final proposition are called premises. The last statement is the conclusion.  The argument is valid if the premises imply the conclusion. An argument form is an argument that is valid no matter what propositions are substituted into its propositional variables.

 If the premises are p1 ,p2, …,pn and the conclusion is q then (p1 p2 pn ) q is a tautology.  Inference rules are all argument simple argument forms that will be used∧ to construct∧ … ∧ more→ complex argument forms.

Rules of Inference for Propositional Logic: Modus Ponens

Corresponding Tautology: (p p q q

∧ ( → )) →

Example: Let p be “It is snowing.” Let q be “I will study discrete math.”

“If it is snowing, then I will study discrete math.” “It is snowing.”

“Therefore , I will study discrete math.”

Modus Tollens

Corresponding Tautology: (¬p p q q

∧( → ))→¬

Example: Let p be “it is snowing.” Let q be “I will study discrete math.”

“If it is snowing, then I will study discrete math.” “I will not study discrete math.”

“Therefore , it is not snowing.”

Hypothetical Syllogism

Corresponding Tautology: ( p q ( r p r)

( → ) ∧ q→ ))→( →

Example: Let p be “it snows.” Let q be “I will study discrete math.” Let r be “I will get an A.”

“If it snows, then I will study discrete math.” “If I study discrete math, I will get an A.”

“Therefore , If it snows, I will get an A.” Disjunctive Syllogism

Corresponding Tautology: (¬p p q q

∧( ∨ ))→

Example: Let p be “I will study discrete math.” Let q be “I will study English literature.”

“I will study discrete math or I will study English literature.” “I will not study discrete math.”

“Therefore , I will study English literature.” Addition

Corresponding Tautology: p p q)

→( ∨

Example: Let p be “I will study discrete math.” Let q be “I will visit Las Vegas.”

“I will study discrete math.”

“Therefore, I will study discrete math or I will visit Las Vegas.”

Simplification

Corresponding Tautology: (p q p

∧ ) → Example: Let p be “I will study discrete math.” Let q be “I will study English literature.”

“I will study discrete math and English literature”

“Therefore, I will study discrete math.”

Conjunction

Corresponding Tautology: ( p) q p q)

( ∧ ( )) →( ∧ Example: Let p be “I will study discrete math.” Let q be “I will study English literature.”

“I will study discrete math.” “I will study English literature.”

“Therefore, I will study discrete math and I will study English literature.” Resolution plays an important role Resolution in AI and is used in Prolog.

Corresponding Tautology: ((¬p r ) p q q r)

∨ ∧ ( ∨ )) →( ∨ Example: Let p be “I will study discrete math.” Let r be “I will study English literature.” Let q be “I will study databases.”

“I will not study discrete math or I will study English literature.” “I will study discrete math or I will study databases.”

“Therefore, I will study databases or I will English literature.” Using the Rules of Inference to Build Valid Arguments  A valid argument is a sequence of statements. Each statement is either a premise or follows from previous statements by rules of inference. The last statement is called conclusion.  A valid argument takes the following form:

S1 S2 . . . Sn

C

Valid Arguments Example 1: From the single proposition

Show that q is a conclusion. Solution: Valid Arguments Example 2:  With these hypotheses: “It is not sunny this afternoon and it is colder than yesterday.” “We will go swimming only if it is sunny.” “If we do not go swimming, then we will take a canoe trip.” “If we take a canoe trip, then we will be home by sunset.”  Using the inference rules, construct a valid argument for the conclusion: “We will be home by sunset.” Solution: ??

Valid Arguments Example 2:  With these hypotheses: “It is not sunny this afternoon and it is colder than yesterday.” “We will go swimming only if it is sunny.” “If we do not go swimming, then we will take a canoe trip.” “If we take a canoe trip, then we will be home by sunset.”  Using the inference rules, construct a valid argument for the conclusion: “We will be home by sunset.” Solution: 1. Choose propositional variables: p : “It is sunny this afternoon.” r : “We will go swimming.” t : “We will be home by sunset.” q : “It is colder than yesterday.” s : “We will take a canoe trip.” 2. Translation into propositional logic:

Continued on next slide  Valid Arguments 3. Construct the Valid Argument Handling Quantified Statements  Valid arguments for quantified statements are a sequence of statements. Each statement is either a premise or follows from previous statements by rules of inference which include:  Rules of Inference for Propositional Logic  Rules of Inference for Quantified Statements  The rules of inference for quantified statements are introduced in the next several slides.

Universal Instantiation (UI)

Example:

Our domain consists of all dogs and Fido is a dog.

“All dogs are cuddly.”

“Therefore, Fido is cuddly.”

Universal Generalization (UG)

Used often implicitly in Mathematical Proofs. Existential Instantiation (EI)

Example:

“There is someone who got an A in the course.” “Let’s call her a and say that a got an A”

Existential Generalization (EG)

Example:

“Michelle got an A in the class.” “Therefore, someone got an A in the class.”

Using Rules of Inference Example 1: Using the rules of inference, construct a valid argument to show that “John Smith has two legs” is a consequence of the premises: “Every man has two legs.” “John Smith is a man.” Solution: Let M(x) denote “x is a man” and L(x) “ x has two legs” and let John Smith be a member of the domain. Valid Argument:

Using Rules of Inference Example 2: Use the rules of inference to construct a valid argument showing that the conclusion “Someone who passed the first exam has not read the book.” follows from the premises “A student in this class has not read the book.” “Everyone in this class passed the first exam.” Solution: Let C(x) denote “x is in this class,” B(x) denote “ x has read the book,” and P(x) denote “x passed the first exam.” First we translate the premises and conclusion into symbolic form.

Continued on next slide 

Using Rules of Inference Valid Argument: Returning to the Socrates Example Solution for Socrates Example

Valid Argument Universal Modus Ponens

Universal Modus Ponens combines universal instantiation and modus ponens into one rule.

This rule could be used in the Socrates example. Section 1.7 Section Summary  Mathematical Proofs  Forms of Theorems  Direct Proofs  Indirect Proofs  Proof of the Contrapositive  Proof by Contradiction

Proofs of Mathematical Statements  A proof is a valid argument that establishes the truth of a statement.  In math, CS, and other disciplines, informal proofs which are generally shorter, are generally used.  More than one rule of inference are often used in a step.  Steps may be skipped.  The rules of inference used are not explicitly stated.  Easier for to understand and to explain to people.  But it is also easier to introduce errors.  Proofs have many practical applications:  verification that computer programs are correct  establishing that operating systems are secure  enabling programs to make inferences in artificial intelligence  showing that system specifications are consistent

Definitions  A theorem is a statement that can be shown to be true using:  definitions  other theorems  axioms (statements which are given as true)  rules of inference  A lemma is a ‘helping theorem’ or a result which is needed to prove a theorem.  A corollary is a result which follows directly from a theorem.  Less important theorems are sometimes called propositions.  A conjecture is a statement that is being proposed to be true. Once a proof of a conjecture is found, it becomes a theorem. It may turn out to be false. Forms of Theorems  Many theorems assert that a property holds for all elements in a domain, such as the integers, the real numbers, or some of the discrete structures that we will study in this class.  Often the universal quantifier (needed for a precise statement of a theorem) is omitted by standard mathematical convention. For example, the statement: “If x > y, where x and y are positive real numbers, then x2 > y2 ” really means “For all positive real numbers x and y, if x > y, then x2 > y2 .” Proving Theorems  Many theorems have the form:

 To prove them, we show that where c is an arbitrary element of the domain,  By universal generalization the truth of the original formula follows.  So, we must prove something of the form:

Proving Conditional Statements: p q

 Trivial Proof: If we know q is true, then p q is true as well. →

“If it is raining→ then 1=1.”

 Vacuous Proof: If we know p is false then p q is true as well. “If I am both rich and poor then 2 + 2 = 5.” → [ Even though these examples seem silly, both trivial and vacuous proofs are often used in mathematical induction, as we will see in Chapter 5) ] Even and Odd Integers Definition: The integer n is even if there exists an integer k such that n = 2k, and n is odd if there exists an integer k, such that n = 2k + 1. Note that every integer is either even or odd and no integer is both even and odd.

We will need this basic fact about the integers in some of the example proofs to follow. We will learn more about the integers in Chapter 4. Proving Conditional Statements: p q  Direct Proof: Assume that p is true. Use rules of inference, axioms, and logical equivalences to show that q must also be true. → Example: Give a direct proof of the theorem “If n is an odd integer, then n2 is odd.” Solution: Assume that n is odd. Then n = 2k + 1 for an integer k. Squaring both sides of the equation, we get: n2 = (2k + 1)2 = 4k2 + 4k k2 + 2k) + 1= 2r + 1, where r = 2k2 + 2k , an integer. We have proved that if n is +1an odd= 2(2 integer, then n2 is an odd integer. ( marks the end of the proof. Sometimes QED is used instead. )

Proving Conditional Statements: p q

 Proof by Contraposition: Assume ¬q and show ¬p is true also. This is sometimes called an indirect proof method. If we give a direct proof of ¬q p then we have a proof of p . → Why does this work? Example→ ¬ : Prove that if n is an integer→ q and 3n + 2 is odd, then n is odd. Solution: Assume n is even. So, n = 2k for some integer k. Thus 3n + k) + 2 =6k k + 1) = 2j for j = 3k +1 Therefore 3n + 2 is even. Since we have shown ¬q p , p q must 2hold = 3(2 as well. If n is an+2 integer = 2(3 and 3n + 2 is odd (not even) , then n is odd (not even). → ¬ →

Proving Conditional Statements: p q Example: Prove that for an integer n, if n2 is odd, then n is odd. Solution: Use proof by contraposition. Assume n is→ even (i.e., not odd). Therefore, there exists an integer k such that n = 2k. Hence, n2 = 4k2 = 2 (2k2) and n2 is even(i.e., not odd). We have shown that if n is an even integer, then n2 is even. Therefore by contraposition, for an integer n, if n2 is odd, then n is odd.

Proving Conditional Statements: p q  Proof by Contradiction: (AKA reductio ad absurdum). To prove p, assume ¬p and derive a contradiction such as p p. Since we have shown→ that ¬p F is true , it follows that the contrapositive T p also∧ ¬ holds.(an indirect form of proof). Example→: Prove that if you pick 22 days from the calendar,→ at least 4 must fall on the same day of the week. Solution: Assume that no more than 3 of the 22 days fall on the same day of the week. Because there are 7 days of the week, we could only have picked 21 days. This contradicts the assumption that we have picked 22 days.

Proof by Contradiction  A preview of Chapter 4. Example: Use a proof by contradiction to give a proof that irrational. Solution: √2a and is b = a/b, where and a and b Suppose √2 is rational. Then there exists integers with √2 b≠ 0 have no common factors (see Chapter 4).a 2Then must be even. If a2 is even then a a is even, a = 2c for some integer c Therefore must be even (an exercise). bSince2 is even. Again then b must be even as .well. Thus, But then 2 must divide both a and b Thereforethat a and b have no common factors. We have proved by contradiction . This contradicts our assumption irrational . that our initial assumption must be false and therefore √2 is

Proof by Contradiction  A preview of Chapter 4. Example: Prove that there is no largest prime number. Solution: Assume that there is a largest prime number. Call it pn. Hence, we can list all the primes 2,3,.., pn. Form

None of the prime numbers on the list divides r. Therefore, by a theorem in Chapter 4, either r is prime or there is a smaller prime that divides r. This contradicts the assumption that there is a largest prime. Therefore, there is no largest prime. Theorems that are Biconditional Statements  To prove a theorem that is a biconditional statement, that is, a statement of the form p q, we show that p q and q p are both true. : Prove the theorem: “If n ↔is an integer, then n is odd→ if and only→ if n2 is odd.” Solution:Example both p q and q p p q. We have already shown (previous slides) that → → . Therefore we can conclude ↔ Sometimes iff is used as an abbreviation for “if an only if,” as in “If n is an integer, then n is odd iif n2 is odd.” What is wrong with this?

“Proof” that 1 = 2

Solution: Step 5. a - b =

0 by the premise and division by 0 is undefined.